#tool-use-mcp

An MCP server is a service that exposes tools, data, or actions to an AI client using the Model Context Protocol, so an LLM-powered app can safely discover and call external capabilities in a standard way. Without a standard protocol, every AI app ends up integrating with files, databases, APIs, and internal tools in its own custom way. That makes integrations harder to build, harder to reuse, and harder to govern. An MCP server solves that by giving AI clients a common interface for: discoverin

The Model Context Protocol (MCP) is an open standard that lets AI apps connect to external tools, data sources, and workflows through a common interface, usually via an MCP client talking to an MCP server. Without a shared protocol, every AI app tends to build one-off integrations for files, databases, APIs, and internal tools. MCP solves that by giving model-powered applications a standard way to discover and use capabilities from different systems. In practice, you reach for MCP when y

Function calling, also called tool calling or structured tool use, is a way for an AI model to ask your app to run a specific function with specific arguments instead of guessing the answer itself. Large language models are good at language, but they are not reliable sources of fresh facts, private data, or side effects like “send the email” or “look up today’s weather.” Function calling solves that by letting the model produce a structured request that your code can execute. You reach f

A tool schema or tool definition is a machine-readable description of a function, API, or action an LLM can call, including its name, what it does, and the inputs it accepts. Tool schemas let you connect a model to real capabilities without letting it invent the interface. In practice, they solve three common problems: Structured calling: the model can ask for a function call instead of writing brittle free-form text. Validation: the runtime can check that the model supplied the

A JSON schema for tool arguments is a machine-readable description of what inputs a tool expects, including the names, types, and constraints of its parameters. When an LLM calls a tool, it needs a reliable way to format the arguments so the tool can actually run. A JSON schema helps by telling the model and the application: which fields are required what type each field should be whether values must match a pattern, range, or enum how to validate the request before executing the tool In practic

Parallel tool calling is when an LLM asks for multiple tools to be run in the same turn, so independent actions can happen at the same time instead of one after another. Use parallel tool calling when the model needs several unrelated results, such as fetching weather for three cities, looking up multiple records, or calling a search API and a calculator in the same step. It matters because it can reduce latency and keep agent flows simpler: rather than forcing the model to serialize independent

A tool result or tool message is the message an app sends back to the model after the model asks it to use an external tool, such as a search API, calculator, database query, or function call. LLMs do not reliably know fresh data, private data, or exact computations on their own. A tool result lets the system return the tool’s output to the model in a structured way so the model can continue the conversation with real evidence. You’d use it whenever the model needs to: fetch data from an

A computer-use or browser-use tool is a capability that lets an AI agent operate software directly—usually a web browser, and sometimes the broader desktop—by clicking, typing, scrolling, and reading the visible screen instead of calling an API. Not every task has a clean API. Many real workflows still live in web apps: logging into dashboards, filling forms, checking inventories, downloading reports, or copying data between systems. A browser-use tool helps an LLM or agent handle those tasks by

A code execution tool, or sandbox, is a controlled runtime that lets an AI agent run code safely without giving it full access to your systems. LLMs are good at writing code, but they cannot reliably verify code by thought alone. A sandbox lets an agent: run generated code check outputs inspect errors iterate on fixes You’d reach for it when the task needs computation, file manipulation, data transformation, or testing. In practice, teams use sandboxes when “just answer in text” is too britt